02005nas a2200169   4500008004100000245006200041210006100103260004200164300001600206520140000222100002801622700002201650700001901672700001701691700002301708856010401731       2001                        eng d00aTask-specific comparison of 3D image registration methods0 aTaskspecific comparison of 3D image registration methods  aBellingham; WashingtonbSPIEc2001///  a1588 - 15983 aWe present a new class of approaches for rigid-body registrationand their evaluation in studying Multiple Sclerosis via multi 
protocol MRI. Two pairs of rigid-body registration algorithms 
were implemented, using cross- correlation and mutual 
information, operating on original gray-level images and on the 
intermediate images resulting from our new scale-based method. 
In the scale image, every voxel has the local scale value 
assigned to it, defined as the radius of the largest sphere 
centered at the voxel with homogeneous intensities. 3D data of 
the head were acquired from 10 MS patients using 6 MRI 
protocols. Images in some of the protocols have been acquired in 
registration. The co-registered pairs were used as ground truth. 
Accuracy and consistency of the 4 registration methods were 
measured within and between protocols for known amounts of 
misregistrations. Our analysis indicates that there is no best 
method. For medium and large misregistration, methods using 
mutual information, for small misregistration, and for the 
consistency tests, correlation methods using the original gray-
level images give the best results. We have previously 
demonstrated the use of local scale information in fuzzy 
connectedness segmentation and image filtering. Scale may also 
have considerable potential for image registration as suggested 
by this work.
1 aNyúl, László, Gábor1 aUdupa, Jayaram, K1 aSaha, Punam, K1 aSonka, Milan1 aHanson, Kenneth, M  uhttps://tisat.inf.u-szeged.hu/publication/task-specific-comparison-of-3d-image-registration-methods01888nas a2200157   4500008004100000245006400041210006300105260004200168300001400210520129900224100002801523700002601551700002201577700002301599856010801622       2000                        eng d00aFuzzy-connected 3D image segmentation at interactive speeds0 aFuzzyconnected 3D image segmentation at interactive speeds  aBellingham; WashingtonbSPIEc2000///  a212 - 2233 aImage segmentation techniques using fuzzy connectednessprinciples have shown their effectiveness in segmenting a 
variety of objects in several large applications in recent 
years. However, one problem with these algorithms has been their 
excessive computational requirements. In an attempt to 
substantially speed them up, in the present paper, we study 
systematically a host of 18 algorithms under two categories -- 
label correcting and label setting. Extensive testing of these 
algorithms on a variety of 3D medical images taken from large 
ongoing applications demonstrates that a 20 - 360 fold 
improvement over current speeds is achievable with a combination 
of algorithms and fast modern PCs. The reliable recognition 
(assisted by human operators) and the accurate, efficient, and 
sophisticated delineation (automatically performed by the 
computer) can be effectively incorporated into a single 
interactive process. If images having intensities with tissue 
specific meaning (such as CT or standardized MR images) are 
utilized, all parameters for the segmentation method can be 
fixed once for all, all intermediate data can be computed before 
the user interaction is needed, and the user can be provided 
with more information at the time of interaction.
1 aNyúl, László, Gábor1 aFalcao, Alexandre, X.1 aUdupa, Jayaram, K1 aHanson, Kenneth, M  uhttps://tisat.inf.u-szeged.hu/publication/fuzzy-connected-3d-image-segmentation-at-interactive-speeds-002106nas a2200169   4500008004100000245009700041210006900138260004200207300001600249520142000265100002201685700002801707700001401735700002401749700002301773856014001796       2000                        eng d00aMultiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies0 aMultiprotocol MR image segmentation in multiple sclerosis experi  aBellingham; WashingtonbSPIEc2000///  a1017 - 10273 aMultiple Sclerosis (MS) is an acquired disease of the centralnervous system. Subjective cognitive and ambulatory test scores 
on a scale called EDSS are currently utilized to assess the 
disease severity. Various MRI protocols are being investigated 
to study the disease based on how it manifests itself in the 
images. In an attempt to eventually replace EDSS by an objective 
measure to assess the natural course of the disease and its 
response to therapy, we have developed image segmentation 
methods based on fuzzy connectedness to quantify various objects 
in multiprotocol MRI. These include the macroscopic objects such 
as lesions, the gray matter (GM), white matter (WM), 
cerebrospinal fluid (CSF), and brain parenchyma as well as the 
microscopic aspects of the diseased WM. Over 1000 studies have 
been processed to date. By far the strongest correlations with 
the clinical measures were demonstrated by the Magnetization 
Transfer Ratio (MTR) histogram parameters obtained for the 
various segmented tissue regions emphasizing the importance of 
considering the microscopic/diffused nature of the disease in 
the individual tissue regions. Brain parenchymal volume also 
demonstrated a strong correlation with the clinical measures 
indicating that brain atrophy is an important indicator of the 
disease. Fuzzy connectedness is a viable segmentation method for 
studying MS.
1 aUdupa, Jayaram, K1 aNyúl, László, Gábor1 aGe, Yiyue1 aGrossman, Robert, J1 aHanson, Kenneth, M  uhttps://tisat.inf.u-szeged.hu/publication/multiprotocol-mr-image-segmentation-in-multiple-sclerosis-experience-with-over-1000-studies-0